Quick fuzzy backpropagation algorithm
Neural Networks
A Fast Simplified Fuzzy ARTMAP Network
Neural Processing Letters
Adaptive anomaly detection with evolving connectionist systems
Journal of Network and Computer Applications - Special issue: Network and information security: A computational intelligence approach
Electric load forecasting using a fuzzy ART&ARTMAP neural network
Applied Soft Computing
Information theoretic novelty detection
Pattern Recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
IEEE Transactions on Neural Networks
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This work presents a methodology to analyze electric power systems transient stability for first swing using a neural network based on adaptive resonance theory (ART) architecture, called Euclidean ARTMAP neural network. The ART architectures present plasticity and stability characteristics, which are very important for the training and to execute the analysis in a fast way. The Euclidean ARTMAP version provides more accurate and faster solutions, when compared to the fuzzy ARTMAP configuration. Three steps are necessary for the network working, training, analysis and continuous training. The training step requires much effort (processing) while the analysis is effectuated almost without computational effort. The proposed network allows approaching several topologies of the electric system at the same time; therefore it is an alternative for real time transient stability of electric power systems. To illustrate the proposed neural network an application is presented for a multi-machine electric power systems composed of 10 synchronous machines, 45 buses and 73 transmission lines.